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Category Archives: G17

Prediction in Economics: a Case Study of Economists’ Views on the 2008 Financial Crisis

By Weiran Zeng

Prediction in economics is the focal point of debate for the future of economics, ever since economists were burdened with the failure to “predict” the 2008 Financial Crisis. This paper discusses positions held by philosophers and economic methodologists regarding what kinds of predictions there are and creates a taxonomy of prediction. Through evaluation of those positions, this paper presents different senses of prediction that can be expected of economics, and assess economists’ reflections according to those senses.

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Advisor: Kevin Hoover | JEL Codes: B41, N1, G17

Multi-Horizon Forecast Optimality Based on Related Forecast Errors

By Christopher G. MacGibbon

This thesis develops a new Multi-Horizon Moment Conditions test for evaluating multi-horizon forecast optimality. The test is based on the variances, covariances and autocovariances of optimal forecast errors that should have a non-zero relationship for multi-horizon forecasts. A simulation study is conducted to determine the test’s size and power properties. Also, the effects of combining the Multi-Horizon Moment Conditions test and the well-known Mincer-Zarnowitz and zero autocorrelation tests into one forecast optimality test are examined. Lastly, an empirical study evaluating forecast optimality for four multi-horizon forecasts made by the Survey of Professional Forecasters is included.

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Advisors: Andrew Patton, Grace Kim and Kent Kimbrough | JEL Codes: G1, G17, G00

Google Search Volume Index: Predicting Returns, Volatility and Trading Volume of Tech Stocks

By Rui Xu

This paper investigates the efficacy of using Google Search Volume Index (SVI), a publicly available tool Google provides via Google Trends, to predict stock movements within the tech sector. Relative changes in weekly search volume index are recorded from April 2004 to March 2015 and correlated with weekly returns, realized volatility and trading volume of 10 actively traded tech stocks. Correlations are drawn for three different time periods, each representing a different stage of the financial business cycle, to find out how Search Volume Index correlates with stock market movements in economic recessions and booms. Google SVI is found to be significantly and positively correlated with trading volume and weekly closing price across 2004 to 2015, and positively correlated with realized volatility from 2009-2015. There exists a positive correlation between weekly stock returns and SVI for half of the stocks sampled across all 3 periods. The regression model was a better fit before and during the recession, suggesting the possibility of stronger “herding” behavior during those periods than in recent years.

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Advisor: Edward Tower | JEL Codes: G1, G14, G17 | Tagged: Analysis, Information, market efficiency, Stock Returns

Conditional Beta Model for Asset Pricing By Sector in the U.S. Equity Markets

By Yuci Zhang

In nance, the beta of an investment is a measure of the risk arising from exposure to general market movements as opposed to idiosyncratic factors. Therefore, reliable estimates of stock portfolio betas are essential for many areas in modern nance, including asset pricing, performance evaluation, and risk management. In this paper, we investigate Static and Dynamic Conditional Correlation (DCC) models for estimating betas by testing them in two asset pricing context, the Capital Asset Pricing Model (CAPM) and Fama-French Three Factor Model. Model precision is evaluated by utilizing the betas to predict out-of-sample portfolio returns within the aforementioned asset-pricing framework. Our findings indicate that DCC-GARCH does consistently have an advantage over the Static model, although with a few exceptions in certain scenarios.

Honors Thesis

Data Set

Advisor: Andrew Patton, Michelle Connolly | JEL Codes: C32, C51, G1, G12, G17 | Tagged: Beta, Asset Pricing, Dynamic Correlation, Equity, U.S. Markets

Beta Estimation Using High Frequency Data

By Angela Ryu

Using high frequency stock price data in estimating nancial measures often causes serious distortion. It is due to the existence of the market microstructure noise, the lag of the observed price to the underlying value due to market friction. The adverse eect of the noise can be avoided by choosing an appropriate sampling frequency. In this study, using mean square error as the measure of accuracy in beta estimation, the optimal pair of sampling frequency and the trailing window was empirically found to be as short as 1 minute and 1 week, respectively. This surprising result may be due to the low market noise resulting from its high liquidity and the econometric properties of the errors-in-variables model. Moreover, the realized beta obtained from the optimal pair outperformed the constant beta from the CAPM when overnight returns were excluded. The comparison further strengthens the argument that the underlying beta is time-varying.

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Advisor: George Tauchen | JEL Codes: C51, C58, G17 | Tagged: Beta estimation, Beta Trailing Window, High-Frequency Data, Market Microstructure Noise, Optimal Sampling Interval, Realized Beta

Questions?

Undergraduate Program Assistant
Jennifer Becker
dus_asst@econ.duke.edu

Director of the Honors Program
Michelle P. Connolly
michelle.connolly@duke.edu